Name
Title | ||
|---|---|---|
Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications |
Abstract | ||
|---|---|---|
We study sample approximations of chance constrained problems. In particular, we consider the sample average approximation (SAA) approach and discuss the convergence properties of the resulting problem. We discuss how one can use the SAA method
to obtain good candidate solutions for chance constrained problems. Numerical experiments are performed to correctly tune
the parameters involved in the SAA. In addition, we present a method for constructing statistical lower bounds for the optimal
value of the considered problem and discuss how one should tune the underlying parameters. We apply the SAA to two chance
constrained problems. The first is a linear portfolio selection problem with returns following a multivariate lognormal distribution.
The second is a joint chance constrained version of a simple blending problem. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10957-009-9523-6 | Journal of Optimization Theory and Applications |
Keywords | Field | DocType |
chance constraints · sample average approximation · portfolio selection,lower bound,lognormal distribution | Sample average approximation,Convergence (routing),Mathematical optimization,Project portfolio management,Upper and lower bounds,Multivariate statistics,Multivariate normal distribution,Log-normal distribution,Mathematics,Statistical analysis | Journal |
Volume | Issue | ISSN |
142 | 2 | 1573-2878 |
Citations | PageRank | References |
99 | 3.83 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| B. K. Pagnoncelli | 1 | 99 | 3.83 |
| Shabbir Ahmed | 2 | 1496 | 104.25 |
| Alexander Shapiro | 3 | 1273 | 147.62 |